SAMPE Journal - November/December 2020 - 11
Very often, the loop is not closed between (4)
Inspection and (1) Design. The proposed automation cycle closes this gap to obtain the complete
AFP workflow.
Figure 9 describes the complete vision of the
neXt AFP workflow, that seamlessly connects all
pillars and ensures that Design is no longer a starting point, but rather a trade in a continuous improvement cycle that integrates Process Planning,
Manufacturing, and Inspection. In summary:
-Design receives parameter optimization from
Inspection and adjusts the design15-16
-Design transmits initial geometry and material definition to Process Planning and receives
back anticipation of what can go wrong17-20
Figure 7. neXt AFP: Manufacturing to inspection.
-Process Planning then forwards to Manufacturing an expected optimized path that meets
the requirements21-22
-Process Planning receives back from Manufacturing the real results as manufactured and
'learns' to optimize for the next cycle1,13,15
-Manufacturing is now 'Smart' and digitized:
a digital twin is capturing data and looking for
data analysis that comes back from Inspection9
-Inspection heavily uses machine learning
techniques and communicates back and forth
with Manufacturing and Design13-14.
Figure 8. Defect measurements as a function of course length normalized
by course width.
The full cycle is the foundation of the neXt AFP
Digital workflow.
HYBRID PHYSICS-DATA BASED MODEL
Previously, in the discussion covering enaction of
a new digital workflow (e.g. next AFP), we presented the macro model of how trades communicate,
interact, and support to create next generation
composites. Another fundamental item needed
for a successful automation and creation of new
composites is moving beyond the physics-based
models. There are several successful attempts at
quantifying a physics-based model for AFP, however the moment a material change is introduced, the
response is no longer 'independent' as models fail.
Wehbe4-5 presented a differential color map that is
geometry based for layups. Such models can be the
starting point for a hybrid model that learns and
adapts based on the collected data, as stated in the
previous workflow.
Integrating data-based approaches will not only
help us to obtain new structures with advanced
composites, but it will break many of the barriers
hindering the adoption of composites due to their
complexity. A hybrid model will require:
w w w. s a m p e . o r g
Figure 9. neXt AFP.
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SAMPE Journal - November/December 2020
Table of Contents for the Digital Edition of SAMPE Journal - November/December 2020
Table of Contents
SAMPE Journal - November/December 2020 - Cover1
SAMPE Journal - November/December 2020 - Cover2
SAMPE Journal - November/December 2020 - Table of Contents
SAMPE Journal - November/December 2020 - 2
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SAMPE Journal - November/December 2020 - Cover3
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